import os
import sys
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re

def main(input_directory, pattern, kind):
    # Set draw_unchanged to True if 'draw_unchanged' file exists in
    # the current directory, else False. If true add a bar for
    # unchanged
    draw_unchanged = os.path.exists('draw_unchanged')

    # Initialize counters
    improvements = 0
    deteriorations = 0
    unchanged = 0

    # Include the '*_DELTA_*.xml3.csv' part in the pattern
    full_pattern = f'{pattern}_DELTA_.*\.xmlv3\.csv'
    
    # Load all *_DELTA_*.xml3.csv files into a single DataFrame
    for filename in os.listdir(input_directory):
        if re.match(full_pattern, filename):
            filepath = os.path.join(input_directory, filename)
            df = pd.read_csv(filepath)

            # Count improvements, deteriorations, and unchanged values
            improvements += df[kind].gt(0).sum()
            deteriorations += df[kind].lt(0).sum()
            unchanged += df[kind].eq(0).sum()

    # If pattern is of the form '(\d+)_.*', the data is by task
    # number. Otherwise if pattern is of the form '.*(\d+)', the data
    # is by CPU load.

    subtitle = ''

    match = re.match(r'(\d+)_.*', pattern)
    if match:
        subtitle = f"Task sets with {match.group(1)} Tasks"
    elif (match := re.match(r'[^\d]*(\d+)$', pattern)):
        subtitle = f"Task sets with {match.group(1)}% CPU load"

    # Adjusting the title to include subtitle if it's not empty
    plot_title = 'Counts of Missed Deadlines (PCP vs PPCP)'
    if subtitle:
        plot_title += f"\n{subtitle}"  # Add the subtitle on a new line if it's not empty
    print (plot_title)
    
    # Create a bar graph
    plt.figure(figsize=(10, 6))
    if draw_unchanged:
        plt.bar(['Improvements', 'Deteriorations', 'Unchanged'], [improvements, deteriorations, unchanged])
    else:
        plt.bar(['Improvements', 'Deteriorations'], [improvements, deteriorations])

    # Increase font size for title and axes labels
    title_fontsize = 16       # Adjust this size as needed
    axes_label_fontsize = 14  # Adjust this size as needed
    plt.title(plot_title, fontsize=title_fontsize)
    plt.xlabel('Difference Categories', fontsize=axes_label_fontsize)
    plt.ylabel('Count', fontsize=axes_label_fontsize)

    # Adjust the tick parameters
    plt.xticks(rotation=0, fontsize=12)  # Adjust fontsize as needed
    plt.yticks(fontsize=12)              # Adjust fontsize as needed
    
    # Adjusting grid and removing margins
    plt.grid(axis='y', linestyle='--', alpha=0.7)
    plt.margins(x=0, y=0)  # This reduces the margins around the plot

    # We want the plot to be tightly fitted
    plt.tight_layout()

    if draw_unchanged:
        bar_values = [improvements, deteriorations, unchanged]
    else:
        bar_values = [improvements, deteriorations]

    # Set threshold for placing annotations inside the bars
    threshold = 0.9 * plt.gca().get_ylim()[1]  # 90% of y-axis limit

    # Display the counts on top of each bar
    for i, v in enumerate(bar_values):
        # Check if the bar is high enough to place annotation inside
        if v > threshold:
            # Place annotation inside the bar near the top
            vertical_alignment = 'top'
            y_offset = -0.05 * plt.gca().get_ylim()[1]  # Slight offset from the top
            text_color = 'white'
        else:
            # Place annotation above the bar
            vertical_alignment = 'bottom'
            y_offset = 0.02 * plt.gca().get_ylim()[1]  # Slight offset above the bar
            text_color = 'black'

        plt.text(i, v + y_offset, str(v), ha='center', va=vertical_alignment, color=text_color)
        #plt.text(i, v + 0.2, str(v), ha='center', va='bottom')

    # Save the plot as a PNG figure in the same directory
    clean_pattern = re.sub(r'[^a-zA-Z0-9_]', '', pattern)
    if draw_unchanged:
        output_filename = f'missed_deadline_counts_{kind}_{clean_pattern}.png'
    else:
        output_filename = f'missed_deadline_counts_{kind}_{clean_pattern}_no_unchanged.png'
    output_filepath = os.path.join(input_directory, output_filename)
    plt.savefig(output_filepath)

    # Show the plot
    #plt.show()

if __name__ == "__main__":
    if len (sys.argv) < 1 or len(sys.argv) > 3:
        print("Usage: python script.py [ input_directory [pattern]  ]")
        sys.exit(1)

    kind = "missed"
    input_directory = sys.argv[1] if len(sys.argv) > 1 else '.'
    pattern = sys.argv[2] if len(sys.argv) > 2 else '.*'

    print (kind, input_directory, pattern)

    main (input_directory, pattern, kind)